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U|Net Autoencoder for Edge|Preserved Denoising of Low Dose ...

U|Net Autoencoder for Edge|Preserved Denoising of Low Dose ...

U-Net Autoencoder for Edge-Preserved Denoising of Low Dose ...

Deep learning-based methods have emerged as a potential solution to this problem. This study proposes a new unsupervised LDCT image denoising algorithm called ...

U-Net Autoencoder for Edge-Preserved Denoising of Low Dose ...

PDF | On Aug 3, 2023, Muhammad Zubair and others published U-Net Autoencoder for Edge-Preserved Denoising of Low Dose Computed Tomography Images: A Novel ...

U-Net Autoencoder for Edge-Preserved Denoising of Low Dose ...

U-Net Autoencoder for Edge-Preserved. Denoising of Low Dose Computed Tomography. Images: A Novel Technique. Muhammad Zubair. Department of Computer and ...

U-Net Autoencoder for Edge-Preserved Denoising of Low Dose ...

U-Net Autoencoder for Edge-Preserved. Denoising of Low Dose Computed Tomography. Images: A Novel Technique. Muhammad Zubair. Department of Computer and ...

A novel denoising method for CT images based on U-net and multi ...

Based on U-network (U-Net) and multi-attention mechanism, a novel denoising method for medical CT images is proposed in this study.

Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising - PMC

Among the autoencoders, denoising autoencoders [16] were developed based on the idea that a good and robust representation can be adaptively learned from ...

Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising

The experimental results on the Mayo low-dose CT dataset demonstrate the utility and robustness of quadratic autoencoder in terms of image denoising and model ...

Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising

The experimental results on the Mayo low-dose CT dataset demonstrate the utility and robustness of quadratic autoencoder in terms of image denoising and ...

Reconstructing and analyzing the invariances of low‐dose CT ...

The proposed method is applied to four popular deep learning-based low-dose CT image denoising networks. We find that the networks are not only ...

Low-Dose CT with a Residual Encoder-Decoder Convolutional ...

stacked sparse denoising autoencoder (SSDA) and its variant were introduced ... 234–241. [49] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional.

Low-dose computed tomography image reconstruction via a ...

Low-Dose CT Image Reconstruction using Vector Quantized Convolutional Autoencoder with Perceptual Loss · U-Net Autoencoder for Edge-Preserved Denoising of Low ...

ERA-WGAT: Edge-enhanced residual autoencoder with a window ...

Although various low-dose CT methods using deep learning techniques have produced impressive results, convolutional neural network based methods ...

CT image denoising methods for image quality improvement and ...

Overall, transformer‐based CT denoising methods have shown promising results in reducing noise while preserving image details, especially in low‐dose CT scans, ...

Innovative Noise Extraction and Denoising in Low-Dose CT Using a ...

Our method significantly enhances LDCT image quality by incorporating multiple attention mechanisms within a U-Net-like architecture. Our approach includes a ...

Innovative Noise Extraction and Denoising in Low-Dose CT ... - OUCI

The AutoEncoder network ensures the preservation of image details and diagnostic integrity. ... This demonstrates that our method has a significant advantage in ...

A review on Deep Learning approaches for low-dose Computed ...

Developing the adaptive denoising algorithms with excellent structure preservation is a significant function in medical imaging, because it ...

Denoising of 3D magnetic resonance images using a residual ...

Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders ...

Advancing healthcare with LDCT image denoising through self ...

... U-net autoencoder for edge-preserved denoising of low dose computed tomography images: a novel technique. In: 2023 13th International Conference on ...

Image Denoising Using Autoencoders in Deep Learning - Omdena

Briefly, the Denoising Autoencoder (DAE) approach is based on the addition of noise to the input image to corrupt the data and mask some of the values, ...

SACNN: Self-Attention Convolutional Neural Network for Low-Dose ...

ERA-WGAT: Edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose CT denoising. Han Liu, Peixi Liao, Hu Chen, ...